Gray-scale Moment Invariants for Airborne Mine Detection, Discrimination and False Alarm Mitigation

Abstract

Shape features based on gray-scale moment invariants are presented for airborne mine detection and discrimination. Eleven shape features are obtained by translation, rotation and contrast normalization of the fourth-order gray-scale moments. Mahalanobis distance between an observed and true (average) shape feature vector is used as a shape metric. Covariance matrix corresponding to the average shape feature vector is obtained analytically using an additive and multiplicative noise model for the MWIR image. Effectiveness of gray scale moment invariant shape features for mine discrimination and false alarm mitigation is shown using MWIR imagery collected for LAMD-I program in May 2000. Successful implementation of the features in an airborne detection depends on the consistency of these shape features over time with change in factors such as solar illumination, ageing, clouds and environmental conditions. A study of the variability of gray-scale moment invariant-based shape features with time is conducted using MWIR time-sequenced imagery acquired in June-July 1998 by E-OIR.

Department(s)

Electrical and Computer Engineering

Keywords and Phrases

Airborne landmine detection; Gray-scale moment invariants; LAMD; Medium wave infrared imaging; Passive infrared imaging; Shape features

International Standard Serial Number (ISSN)

0277-786X

Document Type

Article - Conference proceedings

Document Version

Final Version

File Type

text

Language(s)

English

Rights

© 2024 Society of Photo-optical Instrumentation Engineers, All rights reserved.

Publication Date

01 Jan 2002

Link to Full Text

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